An approach to exploring patterns of imbalance and potential missingness in reports of the randomized baseline values for primary outcomes measurable at baseline in randomized controlled trials for meta-analyses

• Published in: BMC medical research methodology Vol. 22; no. 1; p. 154
• Main Authors: Park, Eun-Gee, Hahn, Seokyung
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 28-05-2022; BioMed Central; BMC
• Subjects: Back pain; Baseline heterogeneity; Baseline imbalance; Bias; Clinical trials; Humans; Intervention; Management; Medical research; Meta-analysis; Primary outcome; Randomized controlled trials; Randomized Controlled Trials as Topic; Systematic review; South Korea; Primary outcome; Baseline heterogeneity; Baseline imbalance; Meta-analysis; Randomized controlled trials
• ISSN: 1471-2288; 1471-2288
• DOI: 10.1186/s12874-022-01620-x

This study evaluated the adequacy of randomization in randomized controlled trials by investigating baseline differences in the primary outcome when a meta-analysis employed an outcome whose baseline level was measurable.
We retrieved Cochrane reviews published during one year. We calculated the proportion of studies that reported randomized baseline values for the primary outcome. The standardized mean difference (SMD) was used to assess baseline imbalance and heterogeneity. We explored ranking-ordered forest plots using a normal cumulative probability curve as a guideline representing well-performed randomized trials. When skewness was suggested, a funnel plot was drawn to assess whether there was a significant linear trend.
In 10 of 18 meta-analyses, more than 25% of trials did not report randomized baseline values of the primary outcomes. Three meta-analyses indicated baseline imbalance (P < 0.1) and three showed substantial heterogeneity (I
 > 60%). Four meta-analyses with forest plots suggesting a skewed SMD distribution also showed a linear trend on their standard errors on the funnel plot.
If the primary outcome is measured at baseline, it is essential to explore the full scope of baseline imbalance among the trials. This could help understand patterns of bias, including missingness, for designing adjustment.